邵俊明：男，電子科技大學教授。受國家留學基金委LMU-CSC（慕尼黑大學-留學基金委）項目資助，於2008年赴德國慕尼黑大學計算機科學系世界著名數據挖掘小組攻讀博士學位。在攻讀博士期間，主要從事數據挖掘的理論研究極其在腦科學等交叉學科的套用研究，其相關論文發表在數據挖掘的三大頂級會議（ACM SIGKDD，IEEE ICDM，SIAM SDM）及權威期刊 IEEE TKDE上。在數據挖掘理論研究的同時，並致力於將其套用於大腦神經影像及水文水資源等交叉學科領域，取得一批原創性研究成果
論文分別發表在相關領域的權威期刊上，如神經科學權威期刊Neurobiology of Aging（IF=6.189，一區期刊），Brain (IF=10.226,一區期刊)；水資源研究領域頂級期刊Water Research（IF=5.323，二區）、權威期刊Environmental Modelling & Software（IF=4.538，二區）等。兩篇研究論文分別被國際數據挖掘ICDM研討會議組和美國IGI Global國際出版社評為“最佳論文獎”。完成博士學位論文“Synchronization-inspired Data Mining”,並於2011年11月提前項目一年左右以最高榮譽（Summa Cum Laude, 0.7分）通過博士論文答辯獲得自然科學博士學位（Dr. rer. Nat.），是慕尼黑大學數據挖掘小組成立以來第二個獲此殊榮的博士畢業生。
基本介紹
人物履歷,教育背景,科研方向,研究項目,論文列表,
人物履歷
畢業後，在慕尼黑工業大學從事關於腦科學挖掘的交叉學科套用研究。2012年8月，獲德國著名的洪堡基金，成為洪堡學者，繼續在德國美因茨大學繼續從事數據挖掘的理論和實踐研究。2013年12月，被電子科技大學引進，在計算機科學與工程學院擔任特聘教授，2014年破格評為博士生導師，成立了數據挖掘實驗室。
教育背景
2013/12 至 今 電子科技大學計算機科學與工程學院 教授
2012/08 2013/12 德國美因茨大學計算機系 博士後（洪堡學者）
2011/11 2012/07 德國慕尼黑工業大學腦科學研究中心 博士後
2008/09 2011/11 德國慕尼黑大學 計算機係數據挖掘中心 博士
2005/09 2008/07 西北農林科技大學 計算機工程學院 碩士
2001/09 2005/07 西北農林科技大學 計算機工程學院 學士
科研方向
小組研究方向主要從事數據挖掘的基礎理論研究和套用研究，主要但不僅限於：
― 基於同步的數據挖據算法研究（聚類、分類、噪聲檢測）
― 大數據環境下數據流的算法研究（概念漂移分析和處理、數據流聚類和分類問題）
― 多源異構網路挖掘（社團挖掘、網路壓縮、動態數據分析）
― 基於數據挖掘的腦科學研究（fMRI/DTI, 結構和功能連線分析，多源學習）
研究項目
主要科研項目：
1.電子科技大學-華為鋰電AI長期合作協定，華為科技有限公司，2020-2022，主持
2.大規模流數據關鍵理論研究及典型套用，中央高校重大項目，2019-2021，主持
3.多模態時空對象分析與可視化，國家科技部重點研發計畫課題，2016-2021，主持
4.複雜演化場景下數據流可靠性學習研究，國家自科基金面上項目，2020-2023，主持
5.大數據環境下基於同步原理的數據流挖掘算法研究，國家自科基金青年項目，2015-2018主持
6.高速數據流挖掘的關鍵問題理論研究及套用，教育部霍英東教育基金會，2018-2021，主持
7.大規模網路挖掘的關鍵技術及套用研究，四川省傑青項目，2016-2019，主持
8.無線電監管大數據平台，成都零點科技有限公司，2017-2018，主持
9.定位黑盒化技術研究一期，華為，2017-2018，主持
10.OSS數據分析1期項目技術開發契約，華為，2018-2019，主持
11.大數據套用升級改造項目智慧型套餐升級，四川電信，2018-2020，主持
論文列表
[1]. Shao, J., Ahmadi, Z. and Kramer, S.：Prototype-based Learning on Concept-drifting Data Streams, Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pp. 412-421. 2014.
[2]. Meng, C., Brandl, F., Tahmasian, M., Shao, J., Manoliu, A., Scherr, M., … & Sorg, C.:Aberrant topology of striatum’s connectivity is associated with the number of episodes in depression, Brain 2014: 137; 598–609.
[3]. Yang, Q., Shao, J., and Scholz, M.：Self–organizing map to estimate sustainable flood retention basin types and variables, Environmental Engineering and Management Journal, 13(1), 129-134, 2014.
[4]. Shao, J., He, X., Boehm, C., Yang, Q. and Plant, C.:Synchronization-inspired Partitioning and Hierarchical Clustering, IEEE Transactions on Knowledge and Data Engineering, 25(4): 893-905. 2013.
[5]. Shao, J, Yang, Q, Wohlschlaeger, A, and Sorg, C.:Insight into Disrupted Spatial Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining, International Journal of Knowledge Discovery in Bioinformatics, 3(1):14-29, 2013.
[6]. Shao, J., He, X., Yang, Q., Plant, C. and Boehm, C.:Robust Synchronization-Based Graph Clustering, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 249-260, 2013.
[7]. Tahmasian, M., Knight, D. C., Manoliu, A., Schwertht'ffer, D., Scherr, M., Meng, C., … & Sorg, C.:Aberrant intrinsic connectivity of hippocampus and amygdala overlap in the fronto-insular and dorsomedial-prefrontal cortex in major depressive disorder, Frontiers in human neuroscience, 7, 2013.
[8]. Shao, J:Synchronization on Data Mining, LAP LAMBERT Academic Publishing, 2012.
[9]. Shao, J., Myers, N., Yang, Q., Feng, J., Plant, C., Böhm, C., Fö;rster, H., Kurz, A., Zimmer, C., Meng, C., Riedl, V., Wohlschlt'ger, A. and Sorg, C.:Prediction of Alzheimer’s disease using individual structural connectivity networks, Neurobiology of Aging, 33(12):2756-2765, 2012.
[10].Shao J., Yang Q., Wohlschlaeger A. and Sorg C.:Discovering Aberrant Patterns of Human Connectome in Alzheimer’s Disease via Subgraph Mining, IEEE International Conference on Data Mining (ICDM), Workshop on Biological Data Mining and its Applications in Healthcare (BioDM), pp. 86-93, 2012.
[11].Plant, C, Thai, SM, Shao, J, Theis, F, Meyer-Baese, A, and Boehm, C:Measuring Non-Gaussianity by Phi-transformed and Fuzzy Histograms, Advances in Artificial Neural Systems, 2012.
[12].Yang, Q, Shao, J, and Scholz, M:Prediction of Sustainable Flood Retention Basin Characteristics using a Self-Organizing Map, Environmental Engineering and Management Journal, 2012.
[13].Yang, Q, Shao, J, Scholz, M, Boehm, C, and Plant, C:Multi-label classification model for Sustainable Flood Retention Basins, Environmental Modelling & Software 32 (2012): 27-36..
[14].Plant, C, Thai, SM, Shao, J, Theis, F, Meyer-Baese, A, and Boehm, C:Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area, Computers, Environment and Urban Systems 36(5): 423-433, 2012.
[15].Shao, J., Yang, Q., Boehm, C. and Plant, C.:Detection of Arbitrarily Oriented Synchronized Clusters in High-dimensional Data, IEEE International Conference on Data Mining (ICDM), pp. 607-616, 2011.
[16].Yang, Q, Scholz, M, and Shao, J:Application of Spatial Statistics as a Screening Tool for Sustainable Flood Retention Basin Management, Water and Environment Journal, 2011.
[17].Yang, Q, Shao, J, Scholz, M, and Plant, C:Feature selection methods for characterizing and classifying adaptive Sustainable Flood Retention Basins, Water Research, 45(3):993-1004, 2011.
[18].Yang, Q, Shao, J, and Scholz, M:Classification of Water Bodies including Sustainable Flood Retention Basins (SFRB), International Conference on Integrated Water Resources Management, pp. 110-111., 2011.
[19].Mueller, N.S., Haegler, K., Shao, J., Plant, C. and Boehm, C.:Weighted Graph Compression for Parameter-free Clustering WithPaCCo, Proceedings of the 2011 SIAM International Conference on Data Mining (SDM), 932-943, 2011.
[20].Boehm, C., Plant, C., Shao, J.* and Yang, Q.:Clustering by synchronization, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), 583-592, 2010.
[21].Shao, J., Boehm, C., Yang, Q. and Plant, C.:Synchronization Based Outlier Detection, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010), 245-260, 2010.
[22].Boehm, C., Feng, J., He, X., Mai, S. M., Plant, C. and Shao, J.:A Novel Similarity Measure for Fiber Clustering using Longest Common Subsequence, ACM SIGKDD Workshop on Data Mining for Medicine and Healthcare (DMMH), pp. 1-9, 2011.
[23].Shao, J., Hahn, K., Yang, Q., Wohlschlaeger, A., Boehm, C., Myers, N. and Plant, C.:Hierarchical Density-based Clustering of White Matter Tracts in the Human Brain, International Journal of Knowledge Discovery in Bioinformatics 1(4), 1-26, 2010.
[24].Shao, J., Hahn, K., Yang, Q., Boehm, C., Wohlschlaeger, A., Myers, N. and Plant, C.:Combining Time Series Similarity with Density-Based Clustering to Identify Fiber Bundles in the Human Brain, Proceedings of International Conference on Data Mining (ICDM), Workshop on Biological Data Mining and its Applications in Healthcare, 747-754, 2010.
[25].Shao, J, Wohlschläger, A, Hahn, C, Boehm, C, and Plant, C.:Density-based Clustering of White Matter Tracts in the Human Brain with Dynamic Time Warping, European Workshop on Mining Massive Data Sets (EMMDS ), pp. 1101-1108,2009.
[26].Shao, J, He, D, and Yang, Q :Multi-semantic Scene Classification Based on Region of Interest, CIMCA/IAWTIC/ISE, pp.732-737,2008.
[27].He, D, Shao, J, Gen, N, and Yang, Q :A Model for Image Categorization Based on Biological Visual Mechanism, New Zealand Journal of Agricultural Research, 50(5) :781-787,2007.
- 教學工作Current Course:Data Mining （數據挖掘）(Spring 2015)，Computer Science.
- UESTC UoG12002: Probability Theory and Mathematical Statistics （機率論與數理統計，全英文教學）(Fall 2014). [10:20-11:55am Tue/Thu @ A313]
